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检索条件"机构=Data Mining and Machine Learning Group"
33 条 记 录,以下是1-10 订阅
排序:
Batch Layer Normalization A new normalization layer for CNNs and RNNs  22
Batch Layer Normalization A new normalization layer for CNNs...
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Proceedings of the 6th International Conference on Advances in Artificial Intelligence
作者: Amir Ziaee Erion ÇAno Design Computing Group TU Wien Austria Research Group Data Mining and Machine Learning University of Vienna Austria
This study introduces a new normalization layer termed Batch Layer Normalization (BLN) to reduce the problem of internal covariate shift in deep neural network layers. As a combined version of batch and layer normaliz... 详细信息
来源: 评论
Motif Discovery Framework for Psychiatric EEG data Classification
arXiv
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arXiv 2025年
作者: Kraljevska, Melanija Hlavackova-Schindler, Katerina Miklautz, Lukas Plant, Claudia Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Währingerstrasse 29 Vienna1090 Austria ds:UniVie University of Vienna Austria
In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identificat... 详细信息
来源: 评论
Topic Segmentation of Research Article Collections
arXiv
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arXiv 2022年
作者: Çano, Erion Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria
Collections of research article data harvested from the web have become common recently since they are important resources for experimenting on tasks such as named entity recognition, text summarization, or keyword ge... 详细信息
来源: 评论
Pattern Discovery in an EEG database of Depression Patients: Preliminary Results  14
Pattern Discovery in an EEG Database of Depression Patients:...
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14th International Conference on Measurement, MEASUREMENT 2023
作者: Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic
The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolo... 详细信息
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Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?
arXiv
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arXiv 2022年
作者: Thoma, Lukas Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria
In recent years, deep neural language models have made strong progress in various NLP tasks. This work explores one facet of the question whether state-of-the-art NLP models exhibit elementary mechanisms known from hu... 详细信息
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hmBERT: Historical Multilingual Language Models for Named Entity Recognition
hmBERT: Historical Multilingual Language Models for Named En...
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2022 Conference and Labs of the Evaluation Forum, CLEF 2022
作者: Schweter, Stefan März, Luisa Schmid, Katharina Çano, Erion Bayerische Staatsbibliothek München Digital Library/ Munich Digitization Center Munich Germany Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria Natural Language Processing Expert Center Data:Lab Volkswagen AG Munich Germany
Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usuall... 详细信息
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SepLL: Separating Latent Class Labels from Weak Supervision Noise
SepLL: Separating Latent Class Labels from Weak Supervision ...
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2022 Findings of the Association for Computational Linguistics: EMNLP 2022
作者: Stephan, Andreas Kougia, Vasiliki Roth, Benjamin Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Vienna Austria
In the weakly supervised learning paradigm, labeling functions automatically assign heuristic, often noisy, labels to data samples. In this work, we provide a method for learning from weak labels by separating two typ... 详细信息
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Cause or Trigger? From Philosophy to Causal Modeling
arXiv
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arXiv 2025年
作者: Hlaváčková-Schindler, Kateřina Wöß, Rainer Pecorino, Vera Schindler, Philip Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Department of Physics and Astronomy University of Catania Catania Italy Faculty of Philosophy University of Vienna Vienna Austria
Not much has been written about the role of triggers in the literature on causal reasoning, causal modeling, or philosophy. In this paper, we focus on describing triggers and causes in the metaphysical sense and on ch... 详细信息
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Deliberative XAI: How Explanations Impact Understanding and Decision-Making of AI Novices in Collective and Individual Settings
arXiv
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arXiv 2024年
作者: Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science Doctoral School Computer Science Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Research Network Data Science Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria
XAI research often focuses on settings where people learn about and assess algorithmic systems individually. However, as more public AI systems are deployed, it becomes essential for XAI to facilitate collective under...
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ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion
arXiv
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arXiv 2023年
作者: Sedova, Anastasiia Roth, Benjamin Research Group Data Mining and Machine Learning University of Vienna Austria UniVie Doctoral School Computer Science University of Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Austria
Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph. Prediction quality can be improved by... 详细信息
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